The present disclosure relates generally to tools, systems and methods for use in radiotherapy treatment delivery and, in particular, to systems and methods for multi-modality imaging-based adaptive planning.
Radiation therapy (RT) has gone through a series of technological revolutions in the last few decades. With intensity modulated RT (IMRT), it became possible to produce highly conformal dose distributions, whereby the bulk radiation dose is delivered within the extent of a tumor. These techniques utilize 3D anatomic and biological information extracted from images of various types (e.g., CT, MRI, PET) acquired a few days prior to the first treatment. However, the locations, shapes and sizes as well as biological properties of the tumor(s), and normal anatomy have been found to change during the course of treatment, primarily due to daily positioning uncertainties of various ROIs and anatomic, physiological and/or clinical factors. The latter include tumor shrinkage, weight loss, volumetric changes in normal organs, non-rigid variations in different bony structures. The traditional assumption that the anatomy discerned from 3D CT images acquired for planning purposes prior to the treatment course is applicable for every fraction may not adequately account for the inter-fractional changes and may limit the ability to fully exploit the potential of highly conformal treatment modalities such as IMRT. This improved capability in dose conformality necessitates better localization of target and organs at risks (OAR), as well as the ability to adapt to inter- or intra-fractional changes, both in the design and in delivery of treatments.
Accurate delivery of radiation therapy necessitates, as a prerequisite, high fidelity, 3D, anatomical images of the patient. For close to two decades, RT planning has utilized CT images as a basis for radiation dose calculation. Due to the linear relationship between Hounsfield units and underlying tissue electron density, CT images permit a voxel-wise correction of radiation dose for differences in tissue attenuation, as well as generation of beam's eye view digitally reconstructed radiographs (DRRs) for treatment verification. The formation of image contrast in CT is primarily due to photoelectric interactions. This effect results in high contrast between tissues of considerably different densities (e.g., bone, lung, and soft tissue). However, adjacent soft tissues (e.g., in brain or abdominal regions) do not possess substantial density differences. The ensuing poor soft tissue contrast on CT images makes delineation of both targets and critical structures extremely challenging. This inability to reliably delineate tumor targets and proximal critical structures on CT images has significant clinical consequences, in that it demands the use of larger margins, which precludes the ability to safely escalate radiation doses due to toxicity constraints of critical structures.
Magnetic resonance imaging (MRI) provides powerful imaging capabilities for cancer diagnosis and treatment. Contrary to CT, MRI is non-ionizing, offers superior soft tissue contrast, and provides a wide array of functional contrast forming mechanisms to characterize tumor physiology. However, unlike CT, the MRI signal bears no direct relationship to electron density, which prevents MRI from being used as a basis for dose calculation. In addition, MR images can be confounded by spatial distortions arising from gradient nonlinearities as well as off-resonance effects. These distortions can be severe, ranging up to several centimeters. Moreover, MR images commonly exhibit regions of non-uniform image intensities, which can introduce inaccuracies in image registration and image segmentation algorithms frequently employed during radiation treatment planning and delivery. These non-uniform image intensities arise out of inhomogeneities in the B1+(RF transmit) field and B1− (RF receive) field when phased-array coils are used for signal reception. Consequently, despite the obvious soft tissue contrast advantages MRI provides, these current issues (e.g., lack of electron density information, sources of geometric distortion, and signal non-uniformities) have hindered the establishment of MRI as a primary imaging modality in radiation oncology.
Adaptive Radiation Therapy (ART) is a state-of-the-art approach that uses a feedback process to account for patient-specific anatomic and/or biological changes during the treatment, thus, delivering highly individualized radiation therapy for cancer patients. Basic components of ART include: (1) detection of anatomic and biological changes, often facilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatment plan optimization to account for the patient-specific spatial morphological and biological changes with consideration of radiation responses, and (3) technologies to precisely deliver the optimized plan to the patient. Interventions of ART may consist of both online and offline approaches.
The inter- and intra-fractional variations, if not accounted for, could result in sub-optimal dose distributions and significant deviations from the original plan, with potentially negative impact in treatment efficiency. Recently, image guided RT (IGRT) has been used widely to correct (eliminate or reduce) the deteriorating effects of the inter-fractional variations. A wide range of correction strategies has been developed based on the available IGRT technologies. The correction methods can be generally classified as “online” or “offline” approaches. Corrections to patient treatment parameters that are performed right after the daily patient information is acquired and before the daily treatment dose is delivered are classified as “online” corrections. This is in contrast to an “offline” correction where the corrective action is made after the daily treatment has been delivered affecting the treatments of subsequent days. Therefore, when an online correction is applied, the delivered daily dose will be the corrected one using the very latest patient set up and anatomic information.
When a patient is set up for each fraction, the anatomy may be different from the one used for the initial treatment planning. Typically the deviations that are most harmful are the so called “systematic” ones, which are also relatively easier to account for by either an offline or online correction strategy. The random component of the deviations, although sometimes less harmful than the systematic deviations, are generally difficult to be fully accounted for and requires an online correction strategy. One of the advantages of online correction strategies over the offline methods is that online strategies can correct for both systematic and random variations. In addition, offline corrections may not be applicable to a course of therapy with a small number of treatment fractions, such as hypo-fractionation or stereotactic body RT (SBRT).
The chief challenge for an online correction strategy is that it needs to be performed within an acceptable time-frame while the patient is lying on the treatment table in the treatment position. This requirement limits a variety of corrective actions that are used as online strategies in today's technology. Consequently, online correction of inter-fractional variations by repositioning the patient based on images acquired immediately before the treatment delivery is the current standard practice for IGRT. Online repositioning strategies practiced in most clinics so far are limited to correct for translational shifts only, failing to account for rotational errors, volume changes and deformations of targets and OARs, and independent motions between different targets/OARs. This is in spite of the fact that the current technology provides enough information to perform much more detailed modifications to the daily treatment than simple translational shifts. In principle, the data required to generate a new treatment plan for the day is available in today's CT-based IGRT practice, but by using the data only for shifting the patient, the full potential of the IGRT is not exploited.
There has been ongoing research to extend online corrections from the table shifting to modifications that can correct for changing anatomy, such as organ rotations and deformations. For example, correction for rotations in addition to translations has been implemented, at least partially, in several technologies (e.g., Tomotherapy). However, other inter-fraction variations may not accounted for and remain a major concern. Such variations can be handled by online re-planning methods, including rapid online plan modifications and full-blown plan re-optimization based on anatomy of the day. For example, quick adjustment of beam aperture shapes and weights based on the CT of the day (the anatomy of the day) is an online plan modification method facilitated by advances in computer technology, which allows computationally intensive operations to be performed within a reasonable time frame. Other examples include GPU-based full-blown re-optimization and adaptation based on pre-computed plan libraries. Consequently, online correction schemes that are more comprehensive than online repositioning are beginning to move into the clinic. However, such online re-planning approaches still present challenges since they require delineation of targets and OARs, which is time a consuming and difficult process to fully automate. As such, manual delineation, or even manual contour validation, on single or multi-modality imaging is the main bottleneck for an online re-planning process to be performed within a few minutes.
Therefore, given the above, there is a need for systems and methods employing multi-modality images for use in adapting and delivering radiotherapy treatments in clinically feasible time frames.